Method for optimizing structure similarity index in video coding

ABSTRACT

The present disclosure provides a computer-implemented method for encoding video. The method includes: generating training data based on one or more video sequences, the training data including a structure similarity index comprising at least one of structure similarity index (SSIM) or multi-scale-structural similarity index (MS-SSIM); training a rate-distortion optimization (RDO) model using the training data; processing the one or more video sequences using the rate-distortion optimization model.

CROSS-REFERENCE TO RELATED APPLICATIONS

The disclosure claims the benefits of priority to U.S. ProvisionalApplication No. 63/012,146, filed on Apr. 18, 2020, which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to video processing, and moreparticularly, to methods and apparatuses for optimizing structuresimilarity index in video coding.

BACKGROUND

A video is a set of static pictures (or “frames”) capturing the visualinformation. To reduce the storage memory and the transmissionbandwidth, a video can be compressed before storage or transmission anddecompressed before display. The compression process is usually referredto as encoding and the decompression process is usually referred to asdecoding. There are various video coding formats which use standardizedvideo coding technologies, most commonly based on prediction, transform,quantization, entropy coding and in-loop filtering. The video codingstandards, such as the High Efficiency Video Coding (HEVC/H.265)standard, the Versatile Video Coding (VVC/H.266) standard, and AVSstandards, specifying the specific video coding formats, are developedby standardization organizations. With more and more advanced videocoding technologies being adopted in the video standards, the codingefficiency of the new video coding standards get higher and higher.

SUMMARY OF THE DISCLOSURE

Embodiments of the present disclosure provide a method for optimizingstructure similarity index in video coding. In some embodiments, themethod includes: generating training data based on one or more videosequences, the training data including a structure similarity indexcomprising at least one of structure similarity index (SSIM) ormulti-scale-structural similarity index (MS-SSIM); training arate-distortion optimization (RDO) model using the training data;processing the one or more video sequences using the rate-distortionoptimization model.

Embodiments of the present disclosure provide an apparatus foroptimizing structure similarity index in video coding. In someembodiments, the apparatus comprising: a memory configured to storeinstructions; and one or more processors communicatively coupled to thememory and configured to execute the instructions to cause the apparatusto perform: generating training data based on one or more videosequences, the training data including a structure similarity indexcomprising at least one of structure similarity index (SSIM) ormulti-scale-structural similarity index (MS-SSIM); training arate-distortion optimization (RDO) model using the training data;processing the one or more video sequences using the rate-distortionoptimization model.

Embodiments of the present disclosure provide a non-transitorycomputer-readable storage medium that stores a set of instructions thatis executable by one or more processors of an apparatus to cause theapparatus to initiate a method for performing video data processing. Insome embodiments, the method includes: generating training data based onone or more video sequences, the training data including a structuresimilarity index comprising at least one of structure similarity index(SSIM) or multi-scale-structural similarity index (MS-S SIM); training arate-distortion optimization (RDO) model using the training data;processing the one or more video sequences using the rate-distortionoptimization model.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments and various aspects of the present disclosure areillustrated in the following detailed description and the accompanyingfigures. Various features shown in the figures are not drawn to scale.

FIG. 1 is a schematic diagram illustrating structures of an examplevideo sequence, according to some embodiments of the present disclosure.

FIG. 2A is a schematic diagram illustrating an exemplary encodingprocess of a hybrid video coding system, consistent with embodiments ofthe disclosure.

FIG. 2B is a schematic diagram illustrating another exemplary encodingprocess of a hybrid video coding system, consistent with embodiments ofthe disclosure.

FIG. 3A is a schematic diagram illustrating an exemplary decodingprocess of a hybrid video coding system, consistent with embodiments ofthe disclosure.

FIG. 3B is a schematic diagram illustrating another exemplary decodingprocess of a hybrid video coding system, consistent with embodiments ofthe disclosure.

FIG. 4 is a block diagram of an exemplary apparatus for encoding ordecoding a video, according to some embodiments of the presentdisclosure.

FIG. 5 shows a flow chart of a method for optimizing structuresimilarity index in video coding, according to some embodiments of thepresent disclosure.

FIG. 6 illustrates an exemplary rate-distortion optimization process,according to some embodiments of the present disclosure.

FIG. 7 shows a flow chart of a method for determining the averagebitrate and the average structure similarity index, according to someembodiments of the present disclosure.

FIG. 8 shows a flow chart of another method determining the averagebitrate and the average structure similarity index, according to someembodiments of the present disclosure.

FIG. 9A and FIG. 9B illustrate exemplary relationships of R-QP and D-QPon the frame level data respectively, according to some embodiments ofthe present disclosure.

FIG. 10A and FIG. 10B illustrate exemplary relationships of R-QP andD-QP on the block level data respectively, according to some embodimentsof the present disclosure.

FIG. 11 shows a flow chart of an example method for block-level MS-SSIMcomputation, according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to exemplary embodiments, examplesof which are illustrated in the accompanying drawings. The followingdescription refers to the accompanying drawings in which the samenumbers in different drawings represent the same or similar elementsunless otherwise represented. The implementations set forth in thefollowing description of exemplary embodiments do not represent allimplementations consistent with the invention. Instead, they are merelyexamples of apparatuses and methods consistent with aspects related tothe invention as recited in the appended claims. Particular aspects ofthe present disclosure are described in greater detail below. The termsand definitions provided herein control, if in conflict with termsand/or definitions incorporated by reference.

The Joint Video Experts Team (WET) of the ITU-T Video Coding ExpertGroup (ITU-T VCEG) and the ISO/IEC Moving Picture Expert Group (ISO/IECMPEG) is currently developing the Versatile Video Coding (VVC/H.266)standard. The VVC standard is aimed at doubling the compressionefficiency of its predecessor, the High Efficiency Video Coding(HEVC/H.265) standard. In other words, VVC's goal is to achieve the samesubjective quality as HEVC/H.265 using half the bandwidth.

To achieve the same subjective quality as HEVC/H.265 using half thebandwidth, the WET has been developing technologies beyond HEVC usingthe joint exploration model (JEM) reference software. As codingtechnologies were incorporated into the JEM, the JEM achievedsubstantially higher coding performance than HEVC.

The VVC standard has been developed recent, and continues to includemore coding technologies that provide better compression performance.VVC is based on the same hybrid video coding system that has been usedin modern video compression standards such as HEVC, H.264/AVC, MPEG2,H.263, etc.

A video is a set of static pictures (or “frames”) arranged in a temporalsequence to store visual information. A video capture device (e.g., acamera) can be used to capture and store those pictures in a temporalsequence, and a video playback device (e.g., a television, a computer, asmartphone, a tablet computer, a video player, or any end-user terminalwith a function of display) can be used to display such pictures in thetemporal sequence. Also, in some applications, a video capturing devicecan transmit the captured video to the video playback device (e.g., acomputer with a monitor) in real-time, such as for surveillance,conferencing, or live broadcasting.

For reducing the storage space and the transmission bandwidth needed bysuch applications, the video can be compressed before storage andtransmission and decompressed before the display. The compression anddecompression can be implemented by software executed by a processor(e.g., a processor of a generic computer) or specialized hardware. Themodule for compression is generally referred to as an “encoder,” and themodule for decompression is generally referred to as a “decoder.” Theencoder and decoder can be collectively referred to as a “codec.” Theencoder and decoder can be implemented as any of a variety of suitablehardware, software, or a combination thereof. For example, the hardwareimplementation of the encoder and decoder can include circuitry, such asone or more microprocessors, digital signal processors (DSPs),application-specific integrated circuits (ASICs), field-programmablegate arrays (FPGAs), discrete logic, or any combinations thereof. Thesoftware implementation of the encoder and decoder can include programcodes, computer-executable instructions, firmware, or any suitablecomputer-implemented algorithm or process fixed in a computer-readablemedium. Video compression and decompression can be implemented byvarious algorithms or standards, such as MPEG-1, MPEG-2, MPEG-4, H.26xseries, or the like. In some applications, the codec can decompress thevideo from a first coding standard and re-compress the decompressedvideo using a second coding standard, in which case the codec can bereferred to as a “transcoder.”

The video encoding process can identify and keep useful information thatcan be used to reconstruct a picture and disregard unimportantinformation for the reconstruction. If the disregarded, unimportantinformation cannot be fully reconstructed, such an encoding process canbe referred to as “lossy.” Otherwise, it can be referred to as“lossless.” Most encoding processes are lossy, which is a tradeoff toreduce the needed storage space and the transmission bandwidth.

The useful information of a picture being encoded (referred to as a“current picture”) include changes with respect to a reference picture(e.g., a picture previously encoded and reconstructed). Such changes caninclude position changes, luminosity changes, or color changes of thepixels, among which the position changes are mostly concerned. Positionchanges of a group of pixels that represent an object can reflect themotion of the object between the reference picture and the currentpicture.

A picture coded without referencing another picture (i.e., it is its ownreference picture) is referred to as an “I-picture.” A picture isreferred to as a “P-picture” if some or all blocks (e.g., blocks thatgenerally refer to portions of the video picture) in the picture arepredicted using intra prediction or inter prediction with one referencepicture (e.g., uni-prediction). A picture is referred to as a“B-picture” if at least one block in it is predicted with two referencepictures (e.g., bi-prediction).

FIG. 1 illustrates structures of an example video sequence 100,according to some embodiments of the present disclosure. Video sequence100 can be a live video or a video having been captured and archived.Video 100 can be a real-life video, a computer-generated video (e.g.,computer game video), or a combination thereof (e.g., a real-life videowith augmented-reality effects). Video sequence 100 can be inputted froma video capture device (e.g., a camera), a video archive (e.g., a videofile stored in a storage device) containing previously captured video,or a video feed interface (e.g., a video broadcast transceiver) toreceive video from a video content provider.

As shown in FIG. 1 , video sequence 100 can include a series of picturesarranged temporally along a timeline, including pictures 102, 104, 106,and 108. Pictures 102-106 are continuous, and there are more picturesbetween pictures 106 and 108. In FIG. 1 , picture 102 is an I-picture,the reference picture of which is picture 102 itself. Picture 104 is aP-picture, the reference picture of which is picture 102, as indicatedby the arrow. Picture 106 is a B-picture, the reference pictures ofwhich are pictures 104 and 108, as indicated by the arrows. In someembodiments, the reference picture of a picture (e.g., picture 104) canbe not immediately preceding or following the picture. For example, thereference picture of picture 104 can be a picture preceding picture 102.It should be noted that the reference pictures of pictures 102-106 areonly examples, and the present disclosure does not limit embodiments ofthe reference pictures as the examples shown in FIG. 1 .

Typically, video codecs do not encode or decode an entire picture at onetime due to the computing complexity of such tasks. Rather, they cansplit the picture into basic segments, and encode or decode the picturesegment by segment. Such basic segments are referred to as basicprocessing units (“BPUs”) in the present disclosure. For example,structure 110 in FIG. 1 shows an example structure of a picture of videosequence 100 (e.g., any of pictures 102-108). In structure 110, apicture is divided into 4×4 basic processing units, the boundaries ofwhich are shown as dash lines. In some embodiments, the basic processingunits can be referred to as “macroblocks” in some video coding standards(e.g., MPEG family, H.261, H.263, or H.264/AVC), or as “coding treeunits” (“CTUs”) in some other video coding standards (e.g., H.265/HEVCor H.266/VVC). The basic processing units can have variable sizes in apicture, such as 128×128, 64×64, 32×32, 16×16, 4×8, 16×32, or anyarbitrary shape and size of pixels. The sizes and shapes of the basicprocessing units can be selected for a picture based on the balance ofcoding efficiency and levels of details to be kept in the basicprocessing unit.

The basic processing units can be logical units, which can include agroup of different types of video data stored in a computer memory(e.g., in a video frame buffer). For example, a basic processing unit ofa color picture can include a luma component (Y) representing achromaticbrightness information, one or more chroma components (e.g., Cb and Cr)representing color information, and associated syntax elements, in whichthe luma and chroma components can have the same size of the basicprocessing unit. The luma and chroma components can be referred to as“coding tree blocks” (“CTBs”) in some video coding standards (e.g.,H.265/HEVC or H.266/VVC). Any operation performed to a basic processingunit can be repeatedly performed to each of its luma and chromacomponents.

Video coding has multiple stages of operations, examples of which areshown in FIGS. 2A-2B and FIGS. 3A-3B. For each stage, the size of thebasic processing units can still be too large for processing, and thuscan be further divided into segments referred to as “basic processingsub-units” in the present disclosure. In some embodiments, the basicprocessing sub-units can be referred to as “blocks” in some video codingstandards (e.g., MPEG family, H.261, H.263, or H.264/AVC), or as “codingunits” (“CUs”) in some other video coding standards (e.g., H.265/HEVC orH.266/VVC). A basic processing sub-unit can have the same or smallersize than the basic processing unit. Similar to the basic processingunits, basic processing sub-units are also logical units, which caninclude a group of different types of video data (e.g., Y, Cb, Cr, andassociated syntax elements) stored in a computer memory (e.g., in avideo frame buffer). Any operation performed to a basic processingsub-unit can be repeatedly performed to each of its luma and chromacomponents. It should be noted that such division can be performed tofurther levels depending on processing needs. It should also be notedthat different stages can divide the basic processing units usingdifferent schemes.

For example, at a mode decision stage (an example of which is shown inFIG. 2B), the encoder can decide what prediction mode (e.g.,intra-picture prediction or inter-picture prediction) to use for a basicprocessing unit, which can be too large to make such a decision. Theencoder can split the basic processing unit into multiple basicprocessing sub-units (e.g., CUs as in H.265/HEVC or H.266/VVC), anddecide a prediction type for each individual basic processing sub-unit.

For another example, at a prediction stage (an example of which is shownin FIGS. 2A-2B), the encoder can perform prediction operation at thelevel of basic processing sub-units (e.g., CUs). However, in some cases,a basic processing sub-unit can still be too large to process. Theencoder can further split the basic processing sub-unit into smallersegments (e.g., referred to as “prediction blocks” or “PBs” inH.265/HEVC or H.266/VVC), at the level of which the prediction operationcan be performed.

For another example, at a transform stage (an example of which is shownin FIGS. 2A-2B), the encoder can perform a transform operation forresidual basic processing sub-units (e.g., CUs). However, in some cases,a basic processing sub-unit can still be too large to process. Theencoder can further split the basic processing sub-unit into smallersegments (e.g., referred to as “transform blocks” or “TBs” in H.265/HEVCor H.266/VVC), at the level of which the transform operation can beperformed. It should be noted that the division schemes of the samebasic processing sub-unit can be different at the prediction stage andthe transform stage. For example, in H.265/HEVC or H.266/VVC, theprediction blocks and transform blocks of the same CU can have differentsizes and numbers.

In structure 110 of FIG. 1 , basic processing unit 112 is furtherdivided into 3×3 basic processing sub-units, the boundaries of which areshown as dotted lines. Different basic processing units of the samepicture can be divided into basic processing sub-units in differentschemes.

In some implementations, to provide the capability of parallelprocessing and error resilience to video encoding and decoding, apicture can be divided into regions for processing, such that, for aregion of the picture, the encoding or decoding process can depend on noinformation from any other region of the picture. In other words, eachregion of the picture can be processed independently. By doing so, thecodec can process different regions of a picture in parallel, thusincreasing the coding efficiency. Also, when data of a region iscorrupted in the processing or lost in network transmission, the codeccan correctly encode or decode other regions of the same picture withoutreliance on the corrupted or lost data, thus providing the capability oferror resilience. In some video coding standards, a picture can bedivided into different types of regions. For example, H.265/HEVC andH.266/VVC provide two types of regions: “slices” and “tiles.” It shouldalso be noted that different pictures of video sequence 100 can havedifferent partition schemes for dividing a picture into regions.

For example, in FIG. 1 , structure 110 is divided into three regions114, 116, and 118, the boundaries of which are shown as solid linesinside structure 110. Region 114 includes four basic processing units.Each of regions 116 and 118 includes six basic processing units. Itshould be noted that the basic processing units, basic processingsub-units, and regions of structure 110 in FIG. 1 are only examples, andthe present disclosure does not limit embodiments thereof.

FIG. 2A illustrates a schematic diagram of an example encoding process200A, consistent with embodiments of the disclosure. For example, theencoding process 200A can be performed by an encoder. As shown in FIG.2A, the encoder can encode video sequence 202 into video bitstream 228according to process 200A. Similar to video sequence 100 in FIG. 1 ,video sequence 202 can include a set of pictures (referred to as“original pictures”) arranged in a temporal order. Similar to structure110 in FIG. 1 , each original picture of video sequence 202 can bedivided by the encoder into basic processing units, basic processingsub-units, or regions for processing. In some embodiments, the encodercan perform process 200A at the level of basic processing units for eachoriginal picture of video sequence 202. For example, the encoder canperform process 200A in an iterative manner, in which the encoder canencode a basic processing unit in one iteration of process 200A. In someembodiments, the encoder can perform process 200A in parallel forregions (e.g., regions 114-118) of each original picture of videosequence 202.

In FIG. 2A, the encoder can feed a basic processing unit (referred to asan “original BPU”) of an original picture of video sequence 202 toprediction stage 204 to generate prediction data 206 and predicted BPU208. The encoder can subtract predicted BPU 208 from the original BPU togenerate residual BPU 210. The encoder can feed residual BPU 210 totransform stage 212 and quantization stage 214 to generate quantizedtransform coefficients 216. The encoder can feed prediction data 206 andquantized transform coefficients 216 to binary coding stage 226 togenerate video bitstream 228. Components 202, 204, 206, 208, 210, 212,214, 216, 226, and 228 can be referred to as a “forward path.” Duringprocess 200A, after quantization stage 214, the encoder can feedquantized transform coefficients 216 to inverse quantization stage 218and inverse transform stage 220 to generate reconstructed residual BPU222. The encoder can add reconstructed residual BPU 222 to predicted BPU208 to generate prediction reference 224, which is used in predictionstage 204 for the next iteration of process 200A. Components 218, 220,222, and 224 of process 200A can be referred to as a “reconstructionpath.” The reconstruction path can be used to ensure that both theencoder and the decoder use the same reference data for prediction.

The encoder can perform process 200A iteratively to encode each originalBPU of the original picture (in the forward path) and generate predictedreference 224 for encoding the next original BPU of the original picture(in the reconstruction path). After encoding all original BPUs of theoriginal picture, the encoder can proceed to encode the next picture invideo sequence 202.

Referring to process 200A, the encoder can receive video sequence 202generated by a video capturing device (e.g., a camera). The term“receive” used herein can refer to receiving, inputting, acquiring,retrieving, obtaining, reading, accessing, or any action in any mannerfor inputting data.

At prediction stage 204, at a current iteration, the encoder can receivean original BPU and prediction reference 224, and perform a predictionoperation to generate prediction data 206 and predicted BPU 208.Prediction reference 224 can be generated from the reconstruction pathof the previous iteration of process 200A. The purpose of predictionstage 204 is to reduce information redundancy by extracting predictiondata 206 that can be used to reconstruct the original BPU as predictedBPU 208 from prediction data 206 and prediction reference 224.

Ideally, predicted BPU 208 can be identical to the original BPU.However, due to non-ideal prediction and reconstruction operations,predicted BPU 208 is generally slightly different from the original BPU.For recording such differences, after generating predicted BPU 208, theencoder can subtract it from the original BPU to generate residual BPU210. For example, the encoder can subtract values (e.g., greyscalevalues or RGB values) of pixels of predicted BPU 208 from values ofcorresponding pixels of the original BPU. Each pixel of residual BPU 210can have a residual value as a result of such subtraction between thecorresponding pixels of the original BPU and predicted BPU 208. Comparedwith the original BPU, prediction data 206 and residual BPU 210 can havefewer bits, but they can be used to reconstruct the original BPU withoutsignificant quality deterioration. Thus, the original BPU is compressed.

To further compress residual BPU 210, at transform stage 212, theencoder can reduce spatial redundancy of residual BPU 210 by decomposingit into a set of two-dimensional “base patterns,” each base patternbeing associated with a “transform coefficient.” The base patterns canhave the same size (e.g., the size of residual BPU 210). Each basepattern can represent a variation frequency (e.g., frequency ofbrightness variation) component of residual BPU 210. None of the basepatterns can be reproduced from any combinations (e.g., linearcombinations) of any other base patterns. In other words, thedecomposition can decompose variations of residual BPU 210 into afrequency domain. Such a decomposition is analogous to a discreteFourier transform of a function, in which the base patterns areanalogous to the base functions (e.g., trigonometry functions) of thediscrete Fourier transform, and the transform coefficients are analogousto the coefficients associated with the base functions.

Different transform algorithms can use different base patterns. Varioustransform algorithms can be used at transform stage 212, such as, forexample, a discrete cosine transform, a discrete sine transform, or thelike. The transform at transform stage 212 is invertible. That is, theencoder can restore residual BPU 210 by an inverse operation of thetransform (referred to as an “inverse transform”). For example, torestore a pixel of residual BPU 210, the inverse transform can bemultiplying values of corresponding pixels of the base patterns byrespective associated coefficients and adding the products to produce aweighted sum. For a video coding standard, both the encoder and decodercan use the same transform algorithm (thus the same base patterns).Thus, the encoder can record only the transform coefficients, from whichthe decoder can reconstruct residual BPU 210 without receiving the basepatterns from the encoder. Compared with residual BPU 210, the transformcoefficients can have fewer bits, but they can be used to reconstructresidual BPU 210 without significant quality deterioration. Thus,residual BPU 210 is further compressed.

The encoder can further compress the transform coefficients atquantization stage 214. In the transform process, different basepatterns can represent different variation frequencies (e.g., brightnessvariation frequencies). Because human eyes are generally better atrecognizing low-frequency variation, the encoder can disregardinformation of high-frequency variation without causing significantquality deterioration in decoding. For example, at quantization stage214, the encoder can generate quantized transform coefficients 216 bydividing each transform coefficient by an integer value (referred to asa “quantization scale factor”) and rounding the quotient to its nearestinteger. After such an operation, some transform coefficients of thehigh-frequency base patterns can be converted to zero, and the transformcoefficients of the low-frequency base patterns can be converted tosmaller integers. The encoder can disregard the zero-value quantizedtransform coefficients 216, by which the transform coefficients arefurther compressed. The quantization process is also invertible, inwhich quantized transform coefficients 216 can be reconstructed to thetransform coefficients in an inverse operation of the quantization(referred to as “inverse quantization”).

Because the encoder disregards the remainders of such divisions in therounding operation, quantization stage 214 can be lossy. Typically,quantization stage 214 can contribute the most information loss inprocess 200A. The larger the information loss is, the fewer bits thequantized transform coefficients 216 can need. For obtaining differentlevels of information loss, the encoder can use different values of thequantization parameter or any other parameter of the quantizationprocess.

At binary coding stage 226, the encoder can encode prediction data 206and quantized transform coefficients 216 using a binary codingtechnique, such as, for example, entropy coding, variable length coding,arithmetic coding, Huffman coding, context-adaptive binary arithmeticcoding, or any other lossless or lossy compression algorithm. In someembodiments, besides prediction data 206 and quantized transformcoefficients 216, the encoder can encode other information at binarycoding stage 226, such as, for example, a prediction mode used atprediction stage 204, parameters of the prediction operation, atransform type at transform stage 212, parameters of the quantizationprocess (e.g., quantization parameters), an encoder control parameter(e.g., a bitrate control parameter), or the like. The encoder can usethe output data of binary coding stage 226 to generate video bitstream228. In some embodiments, video bitstream 228 can be further packetizedfor network transmission.

Referring to the reconstruction path of process 200A, at inversequantization stage 218, the encoder can perform inverse quantization onquantized transform coefficients 216 to generate reconstructed transformcoefficients. At inverse transform stage 220, the encoder can generatereconstructed residual BPU 222 based on the reconstructed transformcoefficients. The encoder can add reconstructed residual BPU 222 topredicted BPU 208 to generate prediction reference 224 that is to beused in the next iteration of process 200A.

It should be noted that other variations of the process 200A can be usedto encode video sequence 202. In some embodiments, stages of process200A can be performed by the encoder in different orders. In someembodiments, one or more stages of process 200A can be combined into asingle stage. In some embodiments, a single stage of process 200A can bedivided into multiple stages. For example, transform stage 212 andquantization stage 214 can be combined into a single stage. In someembodiments, process 200A can include additional stages. In someembodiments, process 200A can omit one or more stages in FIG. 2A.

FIG. 2B illustrates a schematic diagram of another example encodingprocess 200B, consistent with embodiments of the disclosure. Process200B can be modified from process 200A. For example, process 200B can beused by an encoder conforming to a hybrid video coding standard (e.g.,H.26x series). Compared with process 200A, the forward path of process200B additionally includes mode decision stage 230 and dividesprediction stage 204 into spatial prediction stage 2042 and temporalprediction stage 2044. The reconstruction path of process 200Badditionally includes loop filter stage 232 and buffer 234.

Generally, prediction techniques can be categorized into two types:spatial prediction and temporal prediction. Spatial prediction (e.g., anintra-picture prediction or “intra prediction”) can use pixels from oneor more already coded neighboring BPUs in the same picture to predictthe current BPU. That is, prediction reference 224 in the spatialprediction can include the neighboring BPUs. The spatial prediction canreduce the inherent spatial redundancy of the picture. Temporalprediction (e.g., an inter-picture prediction or “inter prediction”) canuse regions from one or more already coded pictures to predict thecurrent BPU. That is, prediction reference 224 in the temporalprediction can include the coded pictures. The temporal prediction canreduce the inherent temporal redundancy of the pictures.

Referring to process 200B, in the forward path, the encoder performs theprediction operation at spatial prediction stage 2042 and temporalprediction stage 2044. For example, at spatial prediction stage 2042,the encoder can perform the intra prediction. For an original BPU of apicture being encoded, prediction reference 224 can include one or moreneighboring BPUs that have been encoded (in the forward path) andreconstructed (in the reconstructed path) in the same picture. Theencoder can generate predicted BPU 208 by extrapolating the neighboringBPUs. The extrapolation technique can include, for example, a linearextrapolation or interpolation, a polynomial extrapolation orinterpolation, or the like. In some embodiments, the encoder can performthe extrapolation at the pixel level, such as by extrapolating values ofcorresponding pixels for each pixel of predicted BPU 208. Theneighboring BPUs used for extrapolation can be located with respect tothe original BPU from various directions, such as in a verticaldirection (e.g., on top of the original BPU), a horizontal direction(e.g., to the left of the original BPU), a diagonal direction (e.g., tothe down-left, down-right, up-left, or up-right of the original BPU), orany direction defined in the used video coding standard. For the intraprediction, prediction data 206 can include, for example, locations(e.g., coordinates) of the used neighboring BPUs, sizes of the usedneighboring BPUs, parameters of the extrapolation, a direction of theused neighboring BPUs with respect to the original BPU, or the like.

For another example, at temporal prediction stage 2044, the encoder canperform the inter prediction. For an original BPU of a current picture,prediction reference 224 can include one or more pictures (referred toas “reference pictures”) that have been encoded (in the forward path)and reconstructed (in the reconstructed path). In some embodiments, areference picture can be encoded and reconstructed BPU by BPU. Forexample, the encoder can add reconstructed residual BPU 222 to predictedBPU 208 to generate a reconstructed BPU. When all reconstructed BPUs ofthe same picture are generated, the encoder can generate a reconstructedpicture as a reference picture. The encoder can perform an operation of“motion estimation” to search for a matching region in a scope (referredto as a “search window”) of the reference picture. The location of thesearch window in the reference picture can be determined based on thelocation of the original BPU in the current picture. For example, thesearch window can be centered at a location having the same coordinatesin the reference picture as the original BPU in the current picture andcan be extended out for a predetermined distance. When the encoderidentifies (e.g., by using a pel-recursive algorithm, a block-matchingalgorithm, or the like) a region similar to the original BPU in thesearch window, the encoder can determine such a region as the matchingregion. The matching region can have different dimensions (e.g., beingsmaller than, equal to, larger than, or in a different shape) from theoriginal BPU. Because the reference picture and the current picture aretemporally separated in the timeline (e.g., as shown in FIG. 1 ), it canbe deemed that the matching region “moves” to the location of theoriginal BPU as time goes by. The encoder can record the direction anddistance of such a motion as a “motion vector.” When multiple referencepictures are used (e.g., as picture 106 in FIG. 1 ), the encoder cansearch for a matching region and determine its associated motion vectorfor each reference picture. In some embodiments, the encoder can assignweights to pixel values of the matching regions of respective matchingreference pictures.

The motion estimation can be used to identify various types of motions,such as, for example, translations, rotations, zooming, or the like. Forinter prediction, prediction data 206 can include, for example,locations (e.g., coordinates) of the matching region, the motion vectorsassociated with the matching region, the number of reference pictures,weights associated with the reference pictures, or the like.

For generating predicted BPU 208, the encoder can perform an operationof “motion compensation.” The motion compensation can be used toreconstruct predicted BPU 208 based on prediction data 206 (e.g., themotion vector) and prediction reference 224. For example, the encodercan move the matching region of the reference picture according to themotion vector, in which the encoder can predict the original BPU of thecurrent picture. When multiple reference pictures are used (e.g., aspicture 106 in FIG. 1 ), the encoder can move the matching regions ofthe reference pictures according to the respective motion vectors andaverage pixel values of the matching regions. In some embodiments, ifthe encoder has assigned weights to pixel values of the matching regionsof respective matching reference pictures, the encoder can add aweighted sum of the pixel values of the moved matching regions.

In some embodiments, the inter prediction can be unidirectional orbidirectional. Unidirectional inter predictions can use one or morereference pictures in the same temporal direction with respect to thecurrent picture. For example, picture 104 in FIG. 1 is a unidirectionalinter-predicted picture, in which the reference picture (e.g., picture102) precedes picture 104. Bidirectional inter predictions can use oneor more reference pictures at both temporal directions with respect tothe current picture. For example, picture 106 in FIG. 1 is abidirectional inter-predicted picture, in which the reference pictures(e.g., pictures 104 and 108) are at both temporal directions withrespect to picture 104.

Still referring to the forward path of process 200B, after spatialprediction 2042 and temporal prediction stage 2044, at mode decisionstage 230, the encoder can select a prediction mode (e.g., one of theintra prediction or the inter prediction) for the current iteration ofprocess 200B. For example, the encoder can perform a rate-distortionoptimization technique, in which the encoder can select a predictionmode to minimize a value of a cost function depending on a bit rate of acandidate prediction mode and distortion of the reconstructed referencepicture under the candidate prediction mode. Depending on the selectedprediction mode, the encoder can generate the corresponding predictedBPU 208 and predicted data 206.

In the reconstruction path of process 200B, if intra prediction mode hasbeen selected in the forward path, after generating prediction reference224 (e.g., the current BPU that has been encoded and reconstructed inthe current picture), the encoder can directly feed prediction reference224 to spatial prediction stage 2042 for later usage (e.g., forextrapolation of a next BPU of the current picture). The encoder canfeed prediction reference 224 to loop filter stage 232, at which theencoder can apply a loop filter to prediction reference 224 to reduce oreliminate distortion (e.g., blocking artifacts) introduced during codingof the prediction reference 224. The encoder can apply various loopfilter techniques at loop filter stage 232, such as, for example,deblocking, sample adaptive offsets, adaptive loop filters, or the like.The loop-filtered reference picture can be stored in buffer 234 (or“decoded picture buffer”) for later use (e.g., to be used as aninter-prediction reference picture for a future picture of videosequence 202). The encoder can store one or more reference pictures inbuffer 234 to be used at temporal prediction stage 2044. In someembodiments, the encoder can encode parameters of the loop filter (e.g.,a loop filter strength) at binary coding stage 226, along with quantizedtransform coefficients 216, prediction data 206, and other information.

FIG. 3A illustrates a schematic diagram of an example decoding process300A, consistent with embodiments of the disclosure. Process 300A can bea decompression process corresponding to the compression process 200A inFIG. 2A. In some embodiments, process 300A can be similar to thereconstruction path of process 200A. A decoder can decode videobitstream 228 into video stream 304 according to process 300A. Videostream 304 can be very similar to video sequence 202. However, due tothe information loss in the compression and decompression process (e.g.,quantization stage 214 in FIGS. 2A-2B), generally, video stream 304 isnot identical to video sequence 202. Similar to processes 200A and 200Bin FIGS. 2A-2B, the decoder can perform process 300A at the level ofbasic processing units (BPUs) for each picture encoded in videobitstream 228. For example, the decoder can perform process 300A in aniterative manner, in which the decoder can decode a basic processingunit in one iteration of process 300A. In some embodiments, the decodercan perform process 300A in parallel for regions (e.g., regions 114-118)of each picture encoded in video bitstream 228.

In FIG. 3A, the decoder can feed a portion of video bitstream 228associated with a basic processing unit (referred to as an “encodedBPU”) of an encoded picture to binary decoding stage 302. At binarydecoding stage 302, the decoder can decode the portion into predictiondata 206 and quantized transform coefficients 216. The decoder can feedquantized transform coefficients 216 to inverse quantization stage 218and inverse transform stage 220 to generate reconstructed residual BPU222. The decoder can feed prediction data 206 to prediction stage 204 togenerate predicted BPU 208. The decoder can add reconstructed residualBPU 222 to predicted BPU 208 to generate predicted reference 224. Insome embodiments, predicted reference 224 can be stored in a buffer(e.g., a decoded picture buffer in a computer memory). The decoder canfeed predicted reference 224 to prediction stage 204 for performing aprediction operation in the next iteration of process 300A.

The decoder can perform process 300A iteratively to decode each encodedBPU of the encoded picture and generate predicted reference 224 forencoding the next encoded BPU of the encoded picture. After decoding allencoded BPUs of the encoded picture, the decoder can output the pictureto video stream 304 for display and proceed to decode the next encodedpicture in video bitstream 228.

At binary decoding stage 302, the decoder can perform an inverseoperation of the binary coding technique used by the encoder (e.g.,entropy coding, variable length coding, arithmetic coding, Huffmancoding, context-adaptive binary arithmetic coding, or any other losslesscompression algorithm). In some embodiments, besides prediction data 206and quantized transform coefficients 216, the decoder can decode otherinformation at binary decoding stage 302, such as, for example, aprediction mode, parameters of the prediction operation, a transformtype, parameters of the quantization process (e.g., quantizationparameters), an encoder control parameter (e.g., a bitrate controlparameter), or the like. In some embodiments, if video bitstream 228 istransmitted over a network in packets, the decoder can depacketize videobitstream 228 before feeding it to binary decoding stage 302.

FIG. 3B illustrates a schematic diagram of another example decodingprocess 300B, consistent with embodiments of the disclosure. Process300B can be modified from process 300A. For example, process 300B can beused by a decoder conforming to a hybrid video coding standard (e.g.,H.26x series). Compared with process 300A, process 300B additionallydivides prediction stage 204 into spatial prediction stage 2042 andtemporal prediction stage 2044, and additionally includes loop filterstage 232 and buffer 234.

In process 300B, for an encoded basic processing unit (referred to as a“current BPU”) of an encoded picture (referred to as a “currentpicture”) that is being decoded, prediction data 206 decoded from binarydecoding stage 302 by the decoder can include various types of data,depending on what prediction mode was used to encode the current BPU bythe encoder. For example, if intra prediction was used by the encoder toencode the current BPU, prediction data 206 can include a predictionmode indicator (e.g., a flag value) indicative of the intra prediction,parameters of the intra prediction operation, or the like. Theparameters of the intra prediction operation can include, for example,locations (e.g., coordinates) of one or more neighboring BPUs used as areference, sizes of the neighboring BPUs, parameters of extrapolation, adirection of the neighboring BPUs with respect to the original BPU, orthe like. For another example, if inter prediction was used by theencoder to encode the current BPU, prediction data 206 can include aprediction mode indicator (e.g., a flag value) indicative of the interprediction, parameters of the inter prediction operation, or the like.The parameters of the inter prediction operation can include, forexample, the number of reference pictures associated with the currentBPU, weights respectively associated with the reference pictures,locations (e.g., coordinates) of one or more matching regions in therespective reference pictures, one or more motion vectors respectivelyassociated with the matching regions, or the like.

Based on the prediction mode indicator, the decoder can decide whetherto perform a spatial prediction (e.g., the intra prediction) at spatialprediction stage 2042 or a temporal prediction (e.g., the interprediction) at temporal prediction stage 2044. The details of performingsuch spatial prediction or temporal prediction are described in FIG. 2Band will not be repeated hereinafter. After performing such spatialprediction or temporal prediction, the decoder can generate predictedBPU 208. The decoder can add predicted BPU 208 and reconstructedresidual BPU 222 to generate prediction reference 224, as described inFIG. 3A.

In process 300B, the decoder can feed predicted reference 224 to spatialprediction stage 2042 or temporal prediction stage 2044 for performing aprediction operation in the next iteration of process 300B. For example,if the current BPU is decoded using the intra prediction at spatialprediction stage 2042, after generating prediction reference 224 (e.g.,the decoded current BPU), the decoder can directly feed predictionreference 224 to spatial prediction stage 2042 for later usage (e.g.,for extrapolation of a next BPU of the current picture). If the currentBPU is decoded using the inter prediction at temporal prediction stage2044, after generating prediction reference 224 (e.g., a referencepicture in which all BPUs have been decoded), the decoder can feedprediction reference 224 to loop filter stage 232 to reduce or eliminatedistortion (e.g., blocking artifacts). The decoder can apply a loopfilter to prediction reference 224, in a way as described in FIG. 2B.The loop-filtered reference picture can be stored in buffer 234 (e.g., adecoded picture buffer in a computer memory) for later use (e.g., to beused as an inter-prediction reference picture for a future encodedpicture of video bitstream 228). The decoder can store one or morereference pictures in buffer 234 to be used at temporal prediction stage2044. In some embodiments, prediction data can further includeparameters of the loop filter (e.g., a loop filter strength). In someembodiments, prediction data includes parameters of the loop filter whenthe prediction mode indicator of prediction data 206 indicates thatinter prediction was used to encode the current BPU.

FIG. 4 is a block diagram of an example apparatus 400 for encoding ordecoding a video, consistent with embodiments of the disclosure. Asshown in FIG. 4 , apparatus 400 can include processor 402. Whenprocessor 402 executes instructions described herein, apparatus 400 canbecome a specialized machine for video encoding or decoding. Processor402 can be any type of circuitry capable of manipulating or processinginformation. For example, processor 402 can include any combination ofany number of a central processing unit (or “CPU”), a graphicsprocessing unit (or “GPU”), a neural processing unit (“NPU”), amicrocontroller unit (“MCU”), an optical processor, a programmable logiccontroller, a microcontroller, a microprocessor, a digital signalprocessor, an intellectual property (IP) core, a Programmable LogicArray (PLA), a Programmable Array Logic (PAL), a Generic Array Logic(GAL), a Complex Programmable Logic Device (CPLD), a Field-ProgrammableGate Array (FPGA), a System On Chip (SoC), an Application-SpecificIntegrated Circuit (ASIC), or the like. In some embodiments, processor402 can also be a set of processors grouped as a single logicalcomponent. For example, as shown in FIG. 4 , processor 402 can includemultiple processors, including processor 402 a, processor 402 b, andprocessor 402 n.

Apparatus 400 can also include memory 404 configured to store data(e.g., a set of instructions, computer codes, intermediate data, or thelike). For example, as shown in FIG. 4 , the stored data can includeprogram instructions (e.g., program instructions for implementing thestages in processes 200A, 200B, 300A, or 300B) and data for processing(e.g., video sequence 202, video bitstream 228, or video stream 304).Processor 402 can access the program instructions and data forprocessing (e.g., via bus 410), and execute the program instructions toperform an operation or manipulation on the data for processing. Memory404 can include a high-speed random-access storage device or anon-volatile storage device. In some embodiments, memory 404 can includeany combination of any number of a random-access memory (RAM), aread-only memory (ROM), an optical disc, a magnetic disk, a hard drive,a solid-state drive, a flash drive, a security digital (SD) card, amemory stick, a compact flash (CF) card, or the like. Memory 404 canalso be a group of memories (not shown in FIG. 4 ) grouped as a singlelogical component.

Bus 410 can be a communication device that transfers data betweencomponents inside apparatus 400, such as an internal bus (e.g., aCPU-memory bus), an external bus (e.g., a universal serial bus port, aperipheral component interconnect express port), or the like.

For ease of explanation without causing ambiguity, processor 402 andother data processing circuits are collectively referred to as a “dataprocessing circuit” in this disclosure. The data processing circuit canbe implemented entirely as hardware, or as a combination of software,hardware, or firmware. In addition, the data processing circuit can be asingle independent module or can be combined entirely or partially intoany other component of apparatus 400.

Apparatus 400 can further include network interface 406 to provide wiredor wireless communication with a network (e.g., the Internet, anintranet, a local area network, a mobile communications network, or thelike). In some embodiments, network interface 406 can include anycombination of any number of a network interface controller (NIC), aradio frequency (RF) module, a transponder, a transceiver, a modem, arouter, a gateway, a wired network adapter, a wireless network adapter,a Bluetooth adapter, an infrared adapter, an near-field communication(“NFC”) adapter, a cellular network chip, or the like.

In some embodiments, optionally, apparatus 400 can further includeperipheral interface 408 to provide a connection to one or moreperipheral devices. As shown in FIG. 4 , the peripheral device caninclude, but is not limited to, a cursor control device (e.g., a mouse,a touchpad, or a touchscreen), a keyboard, a display (e.g., acathode-ray tube display, a liquid crystal display, or a light-emittingdiode display), a video input device (e.g., a camera or an inputinterface coupled to a video archive), or the like.

It should be noted that video codecs (e.g., a codec performing process200A, 200B, 300A, or 300B) can be implemented as any combination of anysoftware or hardware modules in apparatus 400. For example, some or allstages of process 200A, 200B, 300A, or 300B can be implemented as one ormore software modules of apparatus 400, such as program instructionsthat can be loaded into memory 404. For another example, some or allstages of process 200A, 200B, 300A, or 300B can be implemented as one ormore hardware modules of apparatus 400, such as a specialized dataprocessing circuit (e.g., an FPGA, an ASIC, an NPU, or the like).

In video coding scheme, the compression efficiency is jointly evaluatedby the bitrate and the coding distortion between the original and thereconstructed video. Lower bitrate indicates that less bits are consumedto code the video, and hence higher compression efficiency is achieved.Lower distortion implies that less mismatch occurs between thereconstruction video and the original video, and hence higher imagequality can be observed. Therefore, lower bitrate and less distortionare the objects of an efficient video coding framework.

However, for the same codec, compressed bitrate and coding distortionare two factors that need to be balanced. When more bits are consumed,more details can be reserved and hence lower distortion is achieved.Therefore, rate-distortion optimization (RDO) dedicated to achieving theoptimal balance between the rate and the distortion plays a crucial rolein video coding scheme.

In general, to evaluate the joint rate-distortion performance, thefollowing rate-distortion (RD) cost function is used,J=R+λ·D  (1),where R, D, and J denote the rate, the distortion, and the joint cost,respectively. The factor λ is the Lagrangian multiplier, whichrepresents the weight between bitrate and distortion. Alternatively, insome embodiments, the rate-distortion cost function would be alsoexpressed as: J=D+λ·R, as factor λ represents the weight between bitrateand distortion.

Specifically, the calculation of rate-distortion optimization cost maydiffer in different codecs and video coding modules. For example, duringthe mode decision process, full-RD cost is determined to make better RDOdecision where the distortion D is calculated by Sum of SquaredDifference (SSD), in the case where image quality evaluated by PeakSignal to Noise Ratio (PSNR) and the calculation of rate cost considersall the bits consumed. Nevertheless, in the motion estimation,simplified RD cost is usually used. For example, in the HEVC referencesoftware HM and VVC reference software VTM, the distortion cost isdetermined by Sum of Absolute Difference (SAD) in the integer motionestimation stage and by Sum of Absolute Transformed Difference (SATD) inthe fractional motion estimation stage, during which only the bits forcoding motion vector are taken into consideration in the cost of rate.

In the RDO process, one of the key factors is how to design the value ofLagrangian multiplier λ. In the H.264.AVC and HEVC based codecs, twocategories of methods can be used. The first category computes thetheoretical RD cost function based on a given statistic model for videodata. The second category uses an operational RD cost function, which isdetermined based on the data to be compressed, such as context based RDOor Laplace distribution based RDO. No matter which method is used, thefactor λ depends on the codec techniques and calculation method of RDcost.

Although PSNR reveals the objective distortion in terms of video signal,it may not correlate well with human visual system (HVS) and cannotmeasure the images' perceptual distortions well. Therefore, in someembodiments, a structure similarity index (SSIM) based qualityassessment method can be used. SSIM includes three comparisons:luminance, contrast and structure. SSIM can be derived based on thefollowing equation:SSIM=L(x,y)×C(x,y)×S(x,y)  (2).where terms L(x, y), C(x, y), and S(x, y) represent the luminancecomparison, contrast comparison, and structure comparison, respectively,according to the following equations:

$\begin{matrix}{{L( {x,y} )} = \frac{{2\mu_{x}\mu_{y}} + C_{1}}{\mu_{x}^{2} + \mu_{y}^{2} + C_{1}}} & (3)\end{matrix}$ $\begin{matrix}{{C( {x,y} )} = \frac{{2\sigma_{x}\sigma_{y}} + C_{2}}{\sigma_{x}^{2} + \sigma_{y}^{2} + C_{2}}} & (4)\end{matrix}$ $\begin{matrix}{{S( {x,y} )} = {\frac{\sigma_{xy} + C_{3}}{{\sigma_{x}\sigma_{y}} + C_{3}}.}} & (5)\end{matrix}$In the above equations, the quantities x, y are two nonnegative imagesignals. μ_(x) and μ_(y) are the means of x and y, respectively. σ_(x)and σ_(y) are the standard deviations of x and y, respectively, andσ_(xy) is the sample cross-covariance between x and y. C₁, C₂, and C₃are used to stabilize the distortion measure to avoid the denominatorbeing zero or too small. According to some embodiments, C₁=(K₁×L)²,C₂=(K₂×L)² and C₃=C₂/2, where K₁=0.01, K₂=0.03, and L=2^(n)−1 (n is thebit depth), and SSIM can be determined based on the following equation:

$\begin{matrix}{{SSIM} = {\frac{( {{2\mu_{x}\mu_{y}} + C_{1}} )( {{2\sigma_{xy}} + C_{2}} )}{( {\mu_{x}^{2} + \mu_{y}^{2} + C_{1}} )( {\sigma_{x}^{2} + \sigma_{y}^{2} + C_{2}} )}.}} & (6)\end{matrix}$

Based on SSIM, multi-scale-structural similarity index (MS-SSIM) can beused, which is defined byMS−SSIM(x,y)=(L _(M)(x,y)α^(M))Π_(j=1) ^(M) C _(j)(x,y)^(βj) ×S_(j)(x,y)^(γj)  (7).

In equation (7), M is the scale level. M=1 represents the original imagesize, M=2 represents half of the original image size, and so on. In someembodiments, M varies from 1 to 5, β₁=γ₁=0.0448, β₂=γ₂=0.2856,β₃=γ₃=0.3001, β₄=γ₄=0.2363, and α₅=β₅=γ₅=0.1333.

Conventionally, the λ models are established for the codec based onprevious video coding standards, such as H.264/AVC and HEVC. However, nomethod or system has been developed to deploy the rate-distortionoptimization (RDO) model on the VVC based codecs.

Although some RDO methods for the image quality assessed by PSNR areconventionally in use, it is desirable to develop new RDO formula toachieve higher compression performance when the assessment of SSIM orMS-SSIM is used.

The present disclosure provides embodiments to develop the RDO model forthe VVC based codec and other video coding technologies when SSIM orMS-SSIM is used as the assessment method.

FIG. 5 shows a flow chart of a method for optimizing SSIM/MS-SSIM indexin video coding, according to some embodiments of the presentdisclosure. Method 500 can be performed by an encoder (e.g., by process200A of FIG. 2A or 200B of FIG. 2B), a decoder (e.g., by process 300A ofFIG. 3A or 300B of FIG. 3B) or performed by one or more software orhardware components of an apparatus (e.g., apparatus 400 of FIG. 4 ).For example, a processor (e.g., processor 402 of FIG. 4 ) can performmethod 500. In some embodiments, method 500 can be implemented by acomputer program product, embodied in a computer-readable medium,including computer-executable instructions, such as program code,executed by computers (e.g., apparatus 400 of FIG. 4 ). Referring toFIG. 5 , method 500 may include the following steps 502-506.

In step 502, training data based on one or more video sequences isgenerated, wherein the training data includes a structure similarity(SSIM) index or a multi-scale-structural similarity (MS-SSIM) index. Instep 504, a rate-distortion optimization (RDO) model is trained usingthe training data. In step 506, one or more video sequences areprocessed using the RDO model.

In some embodiments, when PSNR is evaluated, the distortion cost isdetermined using the SAD, SATD, or SSD because they can reflect MeanSquare of Error (MSE) directly. For the assessment of SSIM/MS-SSIM, MSEdoes not fit the distortion cost well and hence the following distortioncost function is used:D=n·(1−SSIM)  (8),where n is the number of image samples. Similarly, for MS-SSIM, thedistortion cost can be determined byD=n·(1−MSSSIM)  (9).

For the convenience of explanation, the following description will usethe distortion cost function (8) as an example. However, it iscontemplated that the described principles and methods also work withthe distortion cost function (9).

Combing the rate cost, the overall RD cost function is expressed asJ=R+λ·n·(1−SSIM)  (10).

According to the disclosed embodiments, the multiplier can be determinedbased on a slope between the variances of rate and the variances ofdistortion. Assuming the rate R and the distortion D are differentiableeverywhere as shown in FIG. 6 , the minimum of the RDO cost J is givenby setting its derivative to zero, then

$\begin{matrix}{\lambda = {- {\frac{\partial R}{\partial D}.}}} & (11)\end{matrix}$

In some embodiments, the rate and distortion depend on the exact codectechniques, the to-be-coded video content, and a quantization level thatbalances the ratio between bitrate and reconstruction distortion.Therefore, the function (11) can be expressed as

$\begin{matrix}{{\lambda = {{- \frac{\partial R}{\partial D}} = {- \frac{R( {C,T,{QP}} )}{D( {C,T,{QP}} )}}}},} & (12)\end{matrix}$where C denotes the codec used, T denotes the texture of video content,and QP is the quantization parameter.

In some embodiments, the rate-distortion optimization model is developedbased on the training of to-be-compressed data and the VVC based codecis used. But it is contemplated that the disclosed methods can also beapplied to other codecs. With respect to the video content, it dependson the usage type of the video codec and the training datasetrepresenting the videos in real circumstances may be used. For example,the UGC database released in the compression challenge of IEEEConference on Computer Vision and Pattern Recognition (CVPR) 2020 may beused.

When the video codec and video content are determined, therate-distortion optimization model can be determined using thequantization parameter (which can be determined by quantization stages214 and 218). And the lambda derivation is modelled as

$\begin{matrix}{{\lambda = {{- \frac{\partial{R( {QP} )}}{\partial{D( {QP} )}}} = {- \frac{\frac{\partial R}{{\partial Q}P}}{\frac{\partial D}{{\partial Q}P}}}}}.} & (13)\end{matrix}$

In some embodiments, the rate-distortion optimization model is trainedusing frame level data. In exploring the expressions of ∂R(QP) and∂D(QP), the training videos in dataset are compressed in a wide QPrange, e.g., 15 to 40. The coding bitrates and SSIM/MS-SSIM of eachsequence are recorded and the average values at each QP are determined.According to some embodiments of the present disclosure, two exemplarymethods may be used to determine the average bitrate and the averagestructure similarity index (e.g., the average SSIM or the averageMS-SSIM).

FIG. 7 shows a flow chart of an example method for determining averagebitrate and average structure similarity index, according to someimbibements of present disclosure. Method 700 can be performed by anencoder (e.g., by process 200A of FIG. 2A or 200B of FIG. 2B), a decoder(e.g., by process 300A of FIG. 3A or 300B of FIG. 3B) or performed byone or more software or hardware components of an apparatus (e.g.,apparatus 400 of FIG. 4 ). For example, a processor (e.g., processor 402of FIG. 4 ) can perform method 700. In some embodiments, method 700 canbe implemented by a computer program product, embodied in acomputer-readable medium, including computer-executable instructions,such as program code, executed by computers (e.g., apparatus 400 of FIG.4 ). Referring to FIG. 7 , method 700 may include the following steps702-704.

In step 702, an average bitrate is determined using a sum of the codingbitrates of the one or more video sequences and a number of the one ormore video sequences. That is, the sum of the coding bitrates of the oneor more video sequences is averaged by the number of the one or moresequences. In step 704, an average structure similarity index isdetermined using a sum of the structure similarity indices over framesof the one or more video sequences and the number of the one or morevideo sequences. That is, the sum of the structure similarity indicesover frames of the one or more video sequences is averaged by the numberof the one or more video sequences. Therefore, the training data couldinclude the average bitrate on frame level and the average structuresimilarity index (e.g., average SSIM or average MS-SSIM) on frame level.

FIG. 8 shows a flow chart of another example method for calculatingaverage bitrate and average structure similarity index, according tosome embodiments of the present disclosure. Method 800 can be performedby an encoder (e.g., by process 200A of FIG. 2A or 200B of FIG. 2B), adecoder (e.g., by process 300A of FIG. 3A or 300B of FIG. 3B) orperformed by one or more software or hardware components of an apparatus(e.g., apparatus 400 of FIG. 4 ). For example, a processor (e.g.,processor 402 of FIG. 4 ) can perform method 800. In some embodiments,method 800 can be implemented by a computer program product, embodied ina computer-readable medium, including computer-executable instructions,such as program code, executed by computers (e.g., apparatus 400 of FIG.4 ). Referring to FIG. 8 , method 800 may include the following steps802-804.

When the bitrate is determined per pixel, it can be represented by bitper pixel (bpp). In step 802, the average bit per pixel is determinedusing a sum of the bit per pixel over frames of the one or more videosequences and a number of the one or more video sequences. That is, thesum of the bit per pixel over frames of the one or more video sequencesis averaged by the number of the one or more video sequences. In step804, the average structure similarity index per pixel is determinedusing a sum of the structure similarity indices per pixel over frames ofthe one or more video sequences and the number of the one or more videosequences. That is, the sum of the structure similarity indices perpixel over frames of the one or more video sequences is averaged by thenumber of the one or more video sequences. Therefore, the training datacould include the average bit per pixel on frame level and the averagestructure similarity index (e.g., average SSIM or average MS-SSIM) onframe level. The method 800 may offer better precision.

In some embodiments, the average bitrate and average SSIM per pixel atdifferent QPs are determined. FIG. 9A and FIG. 9B illustrate exemplaryrelationships of R-QP and D-QP, respectively, according to someembodiments of the present disclosure. When the bitrate is computed perpixel, it can be represented by bit per pixel (bpp). As shown in FIG.9A, the graph shows that there can be an exponential relationshipbetween the average bpp and QP. This relationship can be modelled as:

$\begin{matrix}{{\frac{R}{n} = {{bpp} = {p \cdot e^{{- q} \cdot {QP}}}}},} & (14)\end{matrix}$where p and q are the model parameters. In some embodiments, in order toobtain a curve-fitted, the values are 5.92 and 0.167, respectively. Withrespect to the relationship between the distortion and QP, as shown inFIG. 9B, it can be approximately expressed as:

$\begin{matrix}{{\frac{D}{n} = {{1 - {SSIM}} = {a \cdot e^{\frac{{QP} + b}{c}}}}},} & (15)\end{matrix}$where in some embodiments the parameters a, b, and c can be 0.00052,0.238, and 12.05, respectively, for obtaining a curve-fitted. Byincorporating (14) and (15) into (13), the multiplier λ can bedetermined as

$\begin{matrix}{{\lambda = {- \frac{c \cdot q^{2} \cdot e^{{- q} \cdot {QP}}}{a \cdot e^{{({{QP} + b})}/c}}}}.} & (16)\end{matrix}$

In some embodiments, the rate-distortion optimization model is trainedbased on block level data. In the block-based hybrid video codingframework, RDO is conducted on the coding block level, such as thepartitioning mode decision of the CTU (e.g., mode decision 230), themotion estimation for the prediction unit (PU) (e.g., prediction stage204), etc. The methods are similar to method 700 and method 800, and theaverage bitrate and average structure similarity index over blocks aredetermined instead of over frames. If the rate-distortion optimizationmodel is trained based on the encoding data summarized on the blocklevel, more precise and higher R-D performance can be achieved.

The block unit can be selected as CTU, or CU, or other image regionsizes, e.g., 32×32 pixels. In some embodiments, the CTU with a size of128×128 pixels can be used. For each CTU, the consumed bits for thewhole CTU and the SSIM of the CTU are recorded. Similar to theabove-described rate-distortion optimization (RDO) model that is trainedbased on frame level data, the training data could include an averagebitrate over blocks and an average SSIM/MS-SSIM over blocks. The averagebitrate and average SSIM per pixel at different QPs can also bedetermined. When the bitrate is computed per pixel, it can berepresented by bit per pixel (bpp). FIG. 10A and FIG. 10B illustrateexemplary relationships of R-QP and D-QP, respectively, according tosome embodiments of the present disclosure. The curve-fitted R-QP modeland D-QP model are also same as the equations (14) and (15), but thevalues of the parameters in the equations are different. For theCTU-level based R-QP model, in some embodiments, the values of p and qmay be 6.28 and 0.167, respectively. For the CTU-level D-QP model, insome embodiments, the values of a, b, and c may be 0.00045, −0.04, and11.54, respectively. Based on the CTU-level rate-distortion optimizationmodel, in some embodiments the multiplier may be determined as follows:λ=24303×e ^(−(0.25·QP+0.02))  (17).

When MS-SSIM is used as the assessment of the image quality, therate-distortion model may be trained using block-level encoding data.Accordingly, the distortion may only be evaluated by SSIM, rather thanMS-SSIM. The reason is that the calculation of MS-SSIM may not work wellwith small image sizes. To address the above issue, in some embodiments,when MS-SSIM is preferred over SSIM (e.g., in scenarios for determiningthe subjective quality of HVS), a method for block-level MS-SSIMcomputation can be combined into the rate-distortion optimization modelthat is trained based on block level data.

FIG. 11 shows a flow chart of an example method for block-level MS-SSIMcomputation, according to some embodiments of the present disclosure.Method 1100 can be performed by an encoder (e.g., by process 200A ofFIG. 2A or 200B of FIG. 2B), a decoder (e.g., by process 300A of FIG. 3Aor 300B of FIG. 3B) or performed by one or more software or hardwarecomponents of an apparatus (e.g., apparatus 400 of FIG. 4 ). Forexample, a processor (e.g., processor 402 of FIG. 4 ) can perform method1100. In some embodiments, method 800 can be implemented by a computerprogram product, embodied in a computer-readable medium, includingcomputer-executable instructions, such as program code, executed bycomputers (e.g., apparatus 400 of FIG. 4 ). Referring to FIG. 11 ,method 1100 may include the following steps 1102-1104.

In step 1102, a block is divided into one or more windows. Specifically,a window size is no larger than the block size, and there is SSIM onwindow size for each window. In step 1104, the block-level MS-SSIM isdetermined using a sum of the SSIM on window sizes and a number ofwindows. Therefore, the block-level MS-SSIM is obtained according to

$\begin{matrix}{{{{Block}{MS} - {SSIM}} = {\frac{1}{M}{\sum\limits_{i = 1}^{M}{SSIM}_{i}}}},} & (18)\end{matrix}$where M is the count number specifying the number of windows. Assumingthe block size is w×h, the window size can be set to(w/2^(i-1))×(h/2^(i-1)) for M=i. The value of M is not fixed and can beadaptively adjusted by the block size. In some embodiments, the windowsize of the largest M may not be made smaller than 8×8 pixels.

In some embodiments, a non-transitory computer-readable storage mediumincluding instructions is also provided, and the instructions may beexecuted by a device (such as the disclosed encoder and decoder), forperforming the above-described methods. Common forms of non-transitorymedia include, for example, a floppy disk, a flexible disk, hard disk,solid state drive, magnetic tape, or any other magnetic data storagemedium, a CD-ROM, any other optical data storage medium, any physicalmedium with patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROMor any other flash memory, NVRAM, a cache, a register, any other memorychip or cartridge, and networked versions of the same. The device mayinclude one or more processors (CPUs), an input/output interface, anetwork interface, and/or a memory.

It should be noted that, the relational terms herein such as “first” and“second” are used only to differentiate an entity or operation fromanother entity or operation, and do not require or imply any actualrelationship or sequence between these entities or operations. Moreover,the words “comprising,” “having,” “containing,” and “including,” andother similar forms are intended to be equivalent in meaning and be openended in that an item or items following any one of these words is notmeant to be an exhaustive listing of such item or items, or meant to belimited to only the listed item or items.

As used herein, unless specifically stated otherwise, the term “or”encompasses all possible combinations, except where infeasible. Forexample, if it is stated that a database may include A or B, then,unless specifically stated otherwise or infeasible, the database mayinclude A, or B, or A and B. As a second example, if it is stated that adatabase may include A, B, or C, then, unless specifically statedotherwise or infeasible, the database may include A, or B, or C, or Aand B, or A and C, or B and C, or A and B and C.

It is appreciated that the above-described embodiments can beimplemented by hardware, or software (program codes), or a combinationof hardware and software. If implemented by software, it may be storedin the above-described computer-readable media. The software, whenexecuted by the processor can perform the disclosed methods. Thecomputing units and other functional units described in this disclosurecan be implemented by hardware, or software, or a combination ofhardware and software. One of ordinary skill in the art will alsounderstand that multiple ones of the above-described modules/units maybe combined as one module/unit, and each of the above-describedmodules/units may be further divided into a plurality ofsub-modules/sub-units.

The embodiments may further be described using the following clauses:

1. A video data processing method, comprising:

-   -   generating training data based on one or more video sequences,        the training data including a structure similarity index        comprising at least one of structure similarity index (SSIM) or        multi-scale-structural similarity index (MS-SSIM);    -   training a rate-distortion optimization (RDO) model using the        training data;    -   processing the one or more video sequences using the        rate-distortion optimization model.

2. The method of clause 1, wherein the training data comprise arate-distortion cost function dependent on the SSIM.

3. The method of clause 1, wherein the training data comprises arate-distortion cost function dependent on the MS-SSIM.

4. The method of any one of clauses 1 to 3, wherein the rate-distortionoptimization model is based on frame level data.

5. The method of clause 4, wherein the training data comprises anaverage bitrate and an average structure similarity index, the methodfurther comprising:

-   -   determining the average bitrate using a sum of the coding        bitrates of the one or more video sequences and a number of the        one or more video sequences;    -   determining the average structure similarity index using a sum        of the structure similarity indices over frames of the one or        more video sequences and the number of the one or more video        sequences.

6. The method of clause 4, wherein the training data comprise an averagebit per pixel, and an average structure similarity index per pixel, themethod further comprising:

-   -   determining the average bit per pixel using a sum of the bit per        pixel over frames of the one or more video sequences and a        number of the one or more video sequences;    -   determining the average structure similarity index per pixel        over frames of the one or more video sequences using a sum of        the structure similarity indices per pixel and the number of the        one or more video sequences.

7. The method of any one of clauses 1 to 3, wherein the rate-distortionoptimization model is based on block level data.

8. The method of clause 7, wherein the training data comprise an averagebitrate and an average structure similarity index, the method furthercomprising:

-   -   determining the average bitrate using a sum of coding bitrates        and a number of the one or more video sequences;    -   determining the average structure similarity index using a sum        of the structure similarity indices over blocks of the one or        more video sequences and the number of the one or more video        sequence.

9. The method of clause 7, wherein the training data comprise an averagebit per pixel, and an average structure similarity index per pixel, themethod further comprising:

-   -   determining the average bit per pixel using a sum of the bit per        pixel over blocks of the one or more video sequences and a        number of the one or more video sequences;    -   determining the average structure similarity index per pixel        using a sum of the structure similarity indices per pixel over        blocks of the one or more video sequences and the number of the        one or more video sequences.

10. The method of any one of clauses 7 to 9, wherein the training datacomprise the SSIM and the MS-SSIM, and the rate-distortion optimizationmodel comprises a method for block-level MS-SSIM computation, the methodfor block-level MS-SSIM computation comprising:

-   -   dividing a block into one or more windows;    -   determining the block-level MS-SSIM using a sum of SSIM on        window sizes and a number of the windows.

11. The method of clause 10, wherein the window size is set to(w/2^(i-1))×(h/2^(i-1)) for M=i, where w is a width of the block size, his a height of the block size, i is an integer and M is the number ofwindows.

12. The method of clause 11, wherein the window size is not smaller than8×8 pixels.

13. An apparatus for performing video data processing, the apparatuscomprising:

a memory configured to store instructions; and

one or more processors communicatively coupled to the memory andconfigured to execute the instructions to cause the apparatus toperform:

-   -   generating training data based on one or more video sequences,        the training data including a structure similarity index        comprising at least one of structure similarity index (SSIM) or        multi-scale-structural similarity index (MS-SSIM);    -   training a rate-distortion optimization (RDO) model using the        training data;    -   processing the one or more video sequences using the        rate-distortion optimization model.

14. The apparatus of clause 13, wherein the training data comprise arate-distortion cost function dependent on the SSIM.

15. The apparatus of clause 13, wherein the training data comprises arate-distortion cost function dependent on the MS-SSIM.

16. The apparatus of any one of clauses 13 to 15, wherein therate-distortion optimization model is based on frame level data.

17. The apparatus of clause 16, wherein the training data comprises anaverage bitrate and an average structure similarity index, and theprocessor is further configured to execute the instructions to cause theapparatus to perform:

-   -   determining the average bitrate using a sum of the coding        bitrates of the one or more video sequences and a number of the        one or more video sequences;    -   determining the average structure similarity index using a sum        of the structure similarity indices over frames of the one or        more video sequences and the number of the one or more video        sequences.

18. The apparatus of clause 16, wherein the training data comprise anaverage bit per pixel, and an average structure similarity index perpixel, and the processor is further configured to execute theinstructions to cause the apparatus to perform:

-   -   determining the average bit per pixel using a sum of the bit per        pixel over frames of the one or more video sequences and a        number of the one or more video sequences;    -   determining the average structure similarity index per pixel        over frames of the one or more video sequences using a sum of        the structure similarity indices per pixel and the number of the        one or more video sequences.

19. The apparatus of any one of clauses 13 to 15, wherein therate-distortion optimization model is based on block level data.

20. The apparatus of clause 19, wherein the training data comprise anaverage bitrate and an average structure similarity index, and theprocessor is further configured to execute the instructions to cause theapparatus to perform:

-   -   determining the average bitrate using a sum of coding bitrates        and a number of the one or more video sequences;    -   determining the average structure similarity index using a sum        of the structure similarity indices over blocks of the one or        more video sequences and the number of the one or more video        sequence.

21. The apparatus of clause 19, wherein the training data comprise anaverage bit per pixel, and an average structure similarity index perpixel, and the processor is further configured to execute theinstructions to cause the apparatus to perform:

-   -   determining the average bit per pixel using a sum of the bit per        pixel over blocks of the one or more video sequences and a        number of the one or more video sequences;    -   determining the average structure similarity index per pixel        using a sum of the structure similarity indices per pixel over        blocks of the one or more video sequences and the number of the        one or more video sequences.

22. The apparatus of any one of clauses 19 to 21, wherein the trainingdata comprise the SSIM and the MS-SSIM, the rate-distortion optimizationmodel comprises a method for block-level MS-SSIM computation, and theprocessor is further configured to execute the instructions to cause theapparatus to perform:

-   -   dividing a block into one or more windows;    -   determining the block-level MS-SSIM using a sum of SSIM on        window sizes and a number of the windows.

23. The apparatus of clause 22, wherein the window size is set to(w/2^(i-1))×(h/2^(i-1)) for M=i, where w is a width of the block size, his a height of the block size, i is an integer and M is the number ofwindows.

24. The apparatus of clause 23, wherein the window size is not smallerthan 8×8 pixels.

25. A non-transitory computer readable medium that stores a set ofinstructions that is executable by one or more processors of anapparatus to cause the apparatus to initiate a method for performingvideo data processing, the method comprising:

generating training data based on one or more video sequences, thetraining data including a structure similarity index comprising at leastone of structure similarity index (SSIM) or multi-scale-structuralsimilarity index (MS-SSIM);

-   -   training a rate-distortion optimization (RDO) model using the        training data;    -   processing the one or more video sequences using the        rate-distortion optimization model.

26. The non-transitory computer readable medium of clause 25, whereinthe training data comprise a rate-distortion cost function dependent onthe SSIM.

27. The non-transitory computer readable medium of clause 25, whereinthe training data comprises a rate-distortion cost function dependent onthe MS-SSIM.

28. The non-transitory computer readable medium of any one of clauses 25to 27, wherein the rate-distortion optimization model is based on framelevel data.

29. The non-transitory computer readable medium of clause 28, whereinthe training data comprises an average bitrate and an average structuresimilarity index, the method further comprising:

-   -   determining the average bitrate using a sum of the coding        bitrates of the one or more video sequences and a number of the        one or more video sequences;    -   determining the average structure similarity index using a sum        of the structure similarity indices over frames of the one or        more video sequences and the number of the one or more video        sequences.

30. The non-transitory computer readable medium of clause 28, whereinthe training data comprise an average bit per pixel, and an averagestructure similarity index per pixel, the method further comprising:

-   -   determining the average bit per pixel using a sum of the bit per        pixel over frames of the one or more video sequences and a        number of the one or more video sequences;    -   determining the average structure similarity index per pixel        over frames of the one or more video sequences using a sum of        the structure similarity indices per pixel and the number of the        one or more video sequences.

31. The non-transitory computer readable medium of any one of clauses 25to 27, wherein the rate-distortion optimization model is based on blocklevel data.

32. The non-transitory computer readable medium of clause 31, whereinthe training data comprise an average bitrate and an average structuresimilarity index, the method further comprising:

-   -   determining the average bitrate using a sum of coding bitrates        and a number of the one or more video sequences;    -   determining the average structure similarity index using a sum        of the structure similarity indices over blocks of the one or        more video sequences and the number of the one or more video        sequence.

33. The non-transitory computer readable medium of clause 31, whereinthe training data comprise an average bit per pixel, and an averagestructure similarity index per pixel, the method further comprising:

-   -   determining the average bit per pixel using a sum of the bit per        pixel over blocks of the one or more video sequences and a        number of the one or more video sequences;    -   determining the average structure similarity index per pixel        using a sum of the structure similarity indices per pixel over        blocks of the one or more video sequences and the number of the        one or more video sequences.

34. The non-transitory computer readable medium of any one of clauses 31to 33, wherein the training data comprise the SSIM and the MS-SSIM, andthe rate-distortion optimization model comprises a method forblock-level MS-SSIM computation, the method for block-level MS-SSIMcomputation comprising:

-   -   dividing a block into one or more windows;    -   determining the block-level MS-SSIM using a sum of SSIM on        window sizes and a number of the windows.

35. The non-transitory computer readable medium of clause 34, whereinthe window size is set to (w/2^(i-1))×(h/2^(i-1)) for M=i, where w is awidth of the block size, h is a height of the block size, i is aninteger and M is the number of windows.

36. The non-transitory computer readable medium of clause 35, whereinthe window size is not smaller than 8×8 pixels.

In the foregoing specification, embodiments have been described withreference to numerous specific details that can vary from implementationto implementation. Certain adaptations and modifications of thedescribed embodiments can be made. Other embodiments can be apparent tothose skilled in the art from consideration of the specification andpractice of the invention disclosed herein. It is intended that thespecification and examples be considered as exemplary only, with a truescope and spirit of the invention being indicated by the followingclaims. It is also intended that the sequence of steps shown in figuresare only for illustrative purposes and are not intended to be limited toany particular sequence of steps. As such, those skilled in the art canappreciate that these steps can be performed in a different order whileimplementing the same method.

In the drawings and specification, there have been disclosed exemplaryembodiments. However, many variations and modifications can be made tothese embodiments. Accordingly, although specific terms are employed,they are used in a generic and descriptive sense only and not forpurposes of limitation.

What is claimed is:
 1. A video data processing method, comprising:generating training data based on one or more video sequences, thetraining data including a rate-distortion cost function dependent on asimilarity index comprising at least one of structure similarity index(SSIM) or multi-scale structural similarity index (MS-SSIM); training arate-distortion optimization (RDO) model using the training data;processing the one or more video sequences using the rate-distortionoptimization model.
 2. The method of claim 1, wherein therate-distortion optimization model is based on frame level data.
 3. Themethod of claim 2, wherein the training data comprises an averagebitrate and an average structure similarity index, the method furthercomprising: determining the average bitrate using a sum of the codingbitrates of the one or more video sequences and a number of the one ormore video sequences; determining the average structure similarity indexusing a sum of the structure similarity indices over frames of the oneor more video sequences and the number of the one or more videosequences.
 4. The method of claim 2, wherein the training data comprisean average bit per pixel, and an average structure similarity index perpixel, the method further comprising: determining the average bit perpixel using a sum of the bit per pixel over frames of the one or morevideo sequences and a number of the one or more video sequences;determining the average structure similarity index per pixel over framesof the one or more video sequences using a sum of the structuresimilarity indices per pixel and the number of the one or more videosequences.
 5. The method of claim 1, wherein the rate-distortionoptimization model is based on block level data.
 6. The method of claim5, wherein the training data comprise an average bitrate and an averagestructure similarity index, the method further comprising: determiningthe average bitrate using a sum of coding bitrates and a number of theone or more video sequences; determining the average structuresimilarity index using a sum of the structure similarity indices overblocks of the one or more video sequences and the number of the one ormore video sequence.
 7. The method of claim 5, wherein the training datacomprise an average bit per pixel, and an average structure similarityindex per pixel, the method further comprising: determining the averagebit per pixel using a sum of the bit per pixel over blocks of the one ormore video sequences and a number of the one or more video sequences;determining the average structure similarity index per pixel using a sumof the structure similarity indices per pixel over blocks of the one ormore video sequences and the number of the one or more video sequences.8. The method of claim 5, wherein the training data comprise the SSIMand the MS-SSIM, and the rate-distortion optimization model comprises amethod for block-level MS-S SIM computation, the method for block-levelMS-SSIM computation comprising: dividing a block into one or morewindows; determining the block-level MS-SSIM using a sum of SSIM onwindow sizes and a number of the windows.
 9. The method of claim 8,wherein the window size is set to (w/2^(i-1))×(h/2^(i-1)) for M=i, wherew is a width of the block size, h is a height of the block size, i is aninteger and M is the number of windows.
 10. The method of claim 9,wherein the window size is not smaller than 8×8 pixels.
 11. An apparatusfor performing video data processing, the apparatus comprising: a memoryconfigured to store instructions; and one or more processorscommunicatively coupled to the memory and configured to execute theinstructions to cause the apparatus to perform: generating training databased on one or more video sequences, the training data including arate-distortion cost function dependent on a similarity index comprisingat least one of structure similarity index (SSIM) or multi-scalestructural similarity index (MS-SSIM); training a rate-distortionoptimization (RDO) model using the training data; processing the one ormore video sequences using the rate-distortion optimization model. 12.The apparatus of claim 11, wherein the rate-distortion optimizationmodel is based on frame level data.
 13. The apparatus of claim 12,wherein the training data comprises an average bitrate and an averagestructure similarity index, and the processor is further configured toexecute the instructions to cause the apparatus to perform: determiningthe average bitrate using a sum of the coding bitrates of the one ormore video sequences and a number of the one or more video sequences;determining the average structure similarity index using a sum of thestructure similarity indices over frames of the one or more videosequences and the number of the one or more video sequences.
 14. Theapparatus of claim 12, wherein the training data comprise an average bitper pixel, and an average structure similarity index per pixel, and theprocessor is further configured to execute the instructions to cause theapparatus to perform: determining the average bit per pixel using a sumof the bit per pixel over frames of the one or more video sequences anda number of the one or more video sequences; determining the averagestructure similarity index per pixel over frames of the one or morevideo sequences using a sum of the structure similarity indices perpixel and the number of the one or more video sequences.
 15. Theapparatus of claim 11, wherein the rate-distortion optimization model isbased on block level data.
 16. The apparatus of claim 15, wherein thetraining data comprise an average bitrate and an average structuresimilarity index, and the processor is further configured to execute theinstructions to cause the apparatus to perform: determining the averagebitrate using a sum of coding bitrates and a number of the one or morevideo sequences; determining the average structure similarity indexusing a sum of the structure similarity indices over blocks of the oneor more video sequences and the number of the one or more videosequence.
 17. The apparatus of claim 15, wherein the training datacomprise an average bit per pixel, and an average structure similarityindex per pixel, and the processor is further configured to execute theinstructions to cause the apparatus to perform: determining the averagebit per pixel using a sum of the bit per pixel over blocks of the one ormore video sequences and a number of the one or more video sequences;determining the average structure similarity index per pixel using a sumof the structure similarity indices per pixel over blocks of the one ormore video sequences and the number of the one or more video sequences.18. The apparatus of claim 15, wherein the training data comprise theSSIM and the MS-SSIM, the rate-distortion optimization model comprises amethod for block-level MS-SSIM computation, and the processor is furtherconfigured to execute the instructions to cause the apparatus toperform: dividing a block into one or more windows; determining theblock-level MS-SSIM using a sum of SSIM on window sizes and a number ofthe windows.
 19. A non-transitory computer readable medium that stores aset of instructions that is executable by one or more processors of anapparatus to cause the apparatus to initiate a method for performingvideo data processing, the method comprising: generating training databased on one or more video sequences, the training data including arate-distortion cost function dependent on a similarity index comprisingat least one of structure similarity index (SSIM) or multi-scalestructural similarity index (MS-SSIM); training a rate-distortionoptimization (RDO) model using the training data; processing the one ormore video sequences using the rate-distortion optimization model. 20.The non-transitory computer readable medium of claim 19, wherein therate-distortion optimization model is based on frame level data.
 21. Thenon-transitory computer readable medium of claim 19, wherein therate-distortion optimization model is based on block level data.